Accurate prediction of prognosis is critical for therapeutic decisions regarding cancer patients. Many previously developed prognostic scoring systems have limitations in reflecting recent progress in the field of cancer biology such as microarray, next-generation sequencing, and signaling pathways. To develop a new prognostic scoring system for cancer patients, we used mRNA expression and clinical data in various independent breast cancer cohorts (n=1214) from the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) and Gene Expression Omnibus (GEO). A new prognostic score that reflects gene network inherent in genomic big data was calculated using Network-Regularized high-dimensional Cox-regression (Net-score). We compared its discriminatory power with those of two previously used statistical methods: stepwise variable selection via univariate Cox regression (Uni-score) and Cox regression via Elastic net (Enet-score). The Net scoring system showed better discriminatory power in prediction of disease-specific survival (DSS) than other statistical methods (p=0 in METABRIC training cohort, p=0.000331, 4.58e-06 in two METABRIC validation cohorts) when accuracy was examined by log-rank test. Notably, comparison of C-index and AUC values in receiver operating characteristic analysis at 5 years showed fewer differences between training and validation cohorts with the Net scoring system than other statistical methods, suggesting minimal overfitting. The Net-based scoring system also successfully predicted prognosis in various independent GEO cohorts with high discriminatory power. In conclusion, the Net-based scoring system showed better discriminative power than previous statistical methods in prognostic prediction for breast cancer patients. This new system will mark a new era in prognosis prediction for cancer patients.

FELIF is a safe and effective interbody fusion option to decompress the lumbar exiting nerve root and ventral side of dura directly with minimal invasive situation. These slides can be retrieved under Electronic Supplementary Material.

Changes in cervical sagittal parameters were significant after deformity correction in AIS patients. Correlation analysis revealed significant relationships between postoperative radiographic parameters and HRQOL. In particular, T1 slope and C2-C7 SVA were found to be significant predictors of HRQOL in AIS patient.

Purpose Little data are available on the relationship between sagittal spinopelvic parameters and health related quality of life (HRQOL) in ankylosing spondylitis (AS) patients. The aim of this study was to identify the relationships between spinopelvic parameters and HRQOL in AS. Methods The study and control groups comprised 107 AS patients and 40 controls. All underwent anteroposterior and lateral radiographs of the whole spine including hip joints and completed clinical questionnaires. The radiographic parameters examined were sacral slope, pelvic tilt, pelvic incidence, thoracic kyphosis, lumbar lordosis, and sagittal vertical axis. A Visual Analogue Scale (VAS: 0-10) score for back pain, the Oswestry disability index (ODI) questionnaire, Scoliosis Research Society (SRS-22) questionnaire and Bath Ankylosing Spondylitis Disease Activity Index (BASDAI) were administered to evaluate QOL. Statistical analysis was performed to identify significant differences between the study and control groups. In addition, correlations between radiological parameters and clinical questionnaires were sought. Results The AS patients and controls were found to be significantly different in terms of sagittal vertical axis, sacral slope, pelvic tilt, pelvic incidence, and lumbar lordosis. However, no significant intergroup difference was observed for thoracic kyphosis (P [ 0.05). Of the 107 AS patients, there were 18 women and 89 men. Correlation analysis revealed significant relationships between radiographic parameters and clinical outcomes. Multiple regression analysis was performed to identify predictors of clinical outcome, and the results obtained revealed that sagittal vertical axis and sacral slope significantly predicted VAS, ODI and BASDAI scores and that sagittal vertical axis and lumbar lordosis predicted SRS-22 scores. Conclusions AS patients and normal controls were found to be significantly different in terms of sagittal spinopelvic parameters. Correlation analysis revealed significant relationships between radiographic parameters and clinical outcomes. In particular, sagittal vertical axis, sacral slope and lumbar lordosis were found to be significant parameters in prediction of clinical outcomes in AS patient.